Modern physical systems can be exceedingly complex. The complexities of modern systems have led to increasing needs for automated prognosis and fault detection systems. These prognosis and fault detection systems are designed to monitor the system in an effort to predict the future performance of the system and detect potential faults. These systems are designed to detect these potential faults such that the potential faults can be addressed before the potential faults lead to failure in the system.
Physical systems include a variety of mechanical and electrical systems. One type of system where prognosis and fault detection is of particular importance is aircraft systems. In aircraft systems, prognosis and fault detection can detect potential faults such that they can be addressed before they result in serious system failure and possible in-flight shutdowns, take-off aborts, delays or cancellations.
Current prognosis and fault detection systems have relied upon data trending of data from the mechanical system to predict future performance and detect likely faults. In general data trending involves filtering the data to generate a more accurate, filtered estimate of the data. Additionally, data trending can include generating predicted likely future values for the sensor data. Each of these data trending functions facilitates prognosis and fault detection in the mechanical systems.
Current systems have used various statistical techniques for filtering data. As examples, past trending systems have used Kalman filters or exponential filters to filter data. Unfortunately, these current trending systems suffered from many limitations. One particular limitation in Kalman filters is that Kalman filters have typically relied upon certain statistical assumptions. These statistical assumptions may not be valid for some applications. Thus, Kalman filters may not be reasonably applicable to these problems. Another limitation in these current trending systems such as Kalman filters is a lack of accuracy. Thus, these trending systems are unable to accurately determine current sensor values or predict likely future values. This lack of accuracy limits the effectiveness of the prognosis and fault detection system, resulting in potentially unexpected faults and/or false detection of future faults. Other methods such as neural networks and fuzzy logic have also been used for predictions from data. However, these methods generally need careful tuning to the problem at hand. Furthermore, they do not generally provide a quantitative estimate of the reliability of their predictions. Thus, there is a need for improved systems and methods for trending data that offer improved accuracy and reliability.